Agents that take action
The agent calls your APIs itself: the ticket is filed, the record updated. Risky steps wait for a human.
AOur specialty
Agentic AI that finishes the work
Not another chat box. Our agentic AI plans a task, uses your systems and completes it, with guardrails, evaluations and full traces behind every run.
- 24/7agents answering live
- 40+projects delivered
- 12years building software
What we build
Where an agent earns its keep.
An agent is only worth having if you can leave it running without watching it. So we start with one task worth automating, wire the agent into the systems that already do that job, and prove it with tests and traces before it touches a real customer.
Multi-agent orchestration
A planner splits the work between specialists and merges what comes back. A state machine, not a prompt chain.
RAG over your own content
Answers grounded in your own documents, with the citation shown. No source, no answer.
Assistants in your product
Chat, voice or WhatsApp, handing your app structured data it can act on rather than a wall of text.
Workflow automation
Long jobs that survive failure. A run that dies halfway leaves nothing half-written.
Evaluations and guardrails
Every change runs against a test set before it ships. Every run is costed, traced and replayable.
The short list, not the menu.
Most builds mix several of these, plus the parts nobody puts on a slide: migrations, access control, queues and hosting. Tell us the task and we will tell you which pieces it actually needs.
Architecture
How an AI agent actually works.
An agent does not run in a straight line. It thinks, acts, reads what came back, and decides again. Everything else exists to keep that loop safe and reviewable.
in
Request
A message, a call, an API request or a schedule
The loop
ReAct: reason, then act
- 1ThinkThe planner picks the next step from the goal and everything it has learned so far
- 2ActCalls one tool: your API, a database query, or RAG over your documents
- 3ObserveReads the result, checks it against the goal, and decides whether to go again
Repeats until the goal is met, or a guardrail stops it
applied on every step
Guardrails
Permissions, spend limits, approval steps, prompt-injection and PII checks
on success
Action
The write that changes your system of record
always
Traces
Every step, token and cost, replayable and evaluated
The landscape we work in
We build across it, not inside one vendor.
We choose the model and the framework that fit the task, then keep that layer swappable.
OpenAI
Anthropic Claude
Google Gemini
OpenAI Agents SDK
LangGraph
Google ADK
MCP
Postgres + pgvector
Pinecone
WhatsApp Business API
Slack
Telegram
Web chat widget
A better or cheaper model becomes a change of configuration rather than a rebuild. We pick per project, and the parts around the model stay the same.
How we work
From first idea to a working agent.
01
Discover
We choose one task worth automating and map each step. We also agree what the agent may do and what it must pass to a person.
02
Design
We decide which of your systems the agent will use, then set its safety limits and approval steps before we build.
03
Build
We build the agent and test it with realistic conversations, checking the result in your own system each time.
04
Run
We launch it, watch what it does through logs, and improve it as we learn from real use.
Our work
Related work.
Healthcare · AI agent
WhatsApp reception agent for a multi-specialty clinic
An AI agent on WhatsApp that answers patients in Roman Urdu, Urdu and English. It books, cancels and reschedules appointments by itself, at any hour, and connects to a full hospital management system.
Healthcare · AI medical scribe
AI medical scribe: automatic clinical notes
An AI scribe that listens to patient visits and writes clinical notes for the doctor to review. It includes templates, letters, coding support and a full HIPAA compliance programme.
FAQ
Common questions.
How is an AI agent different from a chatbot?
A chatbot answers questions. Agentic AI decides what to do, uses your tools and takes action, such as scheduling a visit or routing a claim, then reports what it did.
What happens when the agent gets something wrong?
Each agent is limited to the actions it needs, and high-risk steps wait for human approval. Anything it is unsure about goes to a person with the conversation attached. Every run is logged step by step, so a mistake can be replayed and fixed.
How do you stop the agent making things up?
RAG grounds every answer in your own content: documents chunked and embedded in a vector database, retrieved with hybrid search and re-ranking, with a citation on each answer. If nothing relevant is found, the agent says so.
What languages can it work in?
We run a live agent in Roman Urdu, Urdu and English, including the way people actually type on WhatsApp. The models handle most major languages, and we test on your users' real messages in every build.
What do you need from us to start?
Access to the systems the agent will use, through your existing APIs or database. The documents it should answer from. And one person who knows how the task is done today.
How long before we see something working?
Usually a few weeks, because we start with one task and a narrow scope. The first version runs with a person approving the actions. Once the traces show it behaving, we widen what it does on its own.
What does an agent cost to build and to run?
Two costs. The build depends on how many of your systems the agent touches and how much checking the work needs. Running cost is per run, the model tokens plus hosting: a few cents for a short task, more for long document or research jobs. You get both figures, measured on a test run, before launch.
Which models and frameworks do you use?
We pick per project and keep the model layer swappable. We have shipped live systems on OpenAI and Claude models, and build with the OpenAI Agents SDK, LangGraph, Google ADK and MCP servers.
Where does our data go?
Retrieval and logs stay in infrastructure you control, and the model sees only what a step needs. We can mask sensitive fields first, and run on a provider tier with no training on your data.
Let’s talk about your project.
Tell us about your AI agent project. We will reply with what we would build first and what it would involve.